| These are all just savings numbers for simplified problems, like vehicle routing problem or simple pickup & delivery problem. Points below properly define problem above: * capacitated pickup and delivery with time windows (NP-hard) * capacity of around 20-60 * every child has to stay maximum T minutes in the bus from the moment it is picked up * each bus can work simultaneously on delivering to multiple schools (covered by standard p&d algorithms) * additional routing/map constraints for roads/vehicle constraints - like minibus drivers being constrained to ares with different housing etc. I'm pretty sure the third point is a tricky one, implementation and speed wise, last one too. I'm skeptical that your API could solve the problem for 600 vehicles off-the-shelf. Not to mention that pickup&delivery slows things down extremely compared to capacitated VRP.
Each optimising step is a venture into heavy graph theory. Or one can use easy heuristics and fail miserably by exploring too little of constrained space. I'm skeptical of these large savings. I've personally worked with some top logistics people that did amazing things with pencils and rulers. Trumping these methods gained savings of about 10-15%. Not to mention the optimizing time. It is impossible (if algorithm is not heavily optimized) to search through enough space for these heavily constrained problems in short amount of time (couple of minutes) to get 60% savings. I might be too critical, but I doubt that Common Lisp algorithm can achieve those kinds of speeds. What if a child was forgotten, how much will the replanning time impact the real world workflow. All sorts of invisible issues. |